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Record W3195810497 · doi:10.3390/ijerph18168819

The Ethics of Dying: Deciphering Pandemic-Resultant Pressures That Influence Elderly Patients’ Medical Assistance in Dying (MAiD) Decisions

2021· article· en· W3195810497 on OpenAlexaff
Masud Khawaja, Abdullah Khawaja

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of British ColumbiaUniversity of the Fraser Valley
Fundersnot available
KeywordsAutonomyContext (archaeology)Mental healthIntervention (counseling)Isolation (microbiology)PandemicState (computer science)CriminologySocial isolationPsychologySociologyPolitical scienceLawMedicineCoronavirus disease 2019 (COVID-19)PsychiatryHistory

Abstract

fetched live from OpenAlex

The objective of medicine is to provide humans with the best possible health outcomes from the beginning to the end of life. If the continuation of life becomes unbearable, some may evaluate procedures to end their lives prematurely. One such procedure is Medical Assistance in Dying (MAiD), and it is hotly contended in many spheres of society. From legal to personal perspectives, there are strong arguments for its implementation and prohibition. This article intends to add to this rich discourse by exploring MAiD in the context of our current pandemic-ridden society as new pressures from social isolation and guilt threaten the autonomy of vulnerable elderly patients. Although autonomy is of chief importance, variables within our current context undermine otherwise independent decisions. Many older individuals are isolated from their social network, resulting in a decline in their mental health. Individuals in such a state are more likely to request a MAiD outcome. Furthermore, overwhelmed healthcare systems may not adequately address this state, which would normally have prompted a mental health intervention. The future of MAiD is far from settled and careful consideration must be given as new contexts come to light, such as those outlined in this paper.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.211
GPT teacher head0.500
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

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